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Mixture-of-Experts

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Showing 76–100 of 1312 papers

TitleStatusHype
Integrating Dynamical Systems Learning with Foundational Models: A Meta-Evolutionary AI Framework for Clinical Trials—0
RankLLM: A Python Package for Reranking with LLMsCode0
I2MoE: Interpretable Multimodal Interaction-aware Mixture-of-ExpertsCode2
On Minimax Estimation of Parameters in Softmax-Contaminated Mixture of Experts—0
TrajMoE: Spatially-Aware Mixture of Experts for Unified Human Mobility Modeling—0
ThanoRA: Task Heterogeneity-Aware Multi-Task Low-Rank AdaptationCode1
μ-MoE: Test-Time Pruning as Micro-Grained Mixture-of-Experts—0
Mod-Adapter: Tuning-Free and Versatile Multi-concept Personalization via Modulation Adapter—0
Guiding the Experts: Semantic Priors for Efficient and Focused MoE RoutingCode0
EvidenceMoE: A Physics-Guided Mixture-of-Experts with Evidential Critics for Advancing Fluorescence Light Detection and Ranging in Scattering Media—0
JanusDNA: A Powerful Bi-directional Hybrid DNA Foundation ModelCode1
DualComp: End-to-End Learning of a Unified Dual-Modality Lossless Compressor—0
DriveMoE: Mixture-of-Experts for Vision-Language-Action Model in End-to-End Autonomous Driving—0
Not All Models Suit Expert Offloading: On Local Routing Consistency of Mixture-of-Expert ModelsCode0
CoLA: Collaborative Low-Rank AdaptationCode0
Time Tracker: Mixture-of-Experts-Enhanced Foundation Time Series Forecasting Model with Decoupled Training Pipelines—0
MoRE-Brain: Routed Mixture of Experts for Interpretable and Generalizable Cross-Subject fMRI Visual DecodingCode0
Hunyuan-TurboS: Advancing Large Language Models through Mamba-Transformer Synergy and Adaptive Chain-of-Thought—0
Efficient Data Driven Mixture-of-Expert Extraction from Trained Networks—0
Multimodal Cultural Safety: Evaluation Frameworks and Alignment StrategiesCode0
Balanced and Elastic End-to-end Training of Dynamic LLMs—0
Multimodal Mixture of Low-Rank Experts for Sentiment Analysis and Emotion Recognition—0
THOR-MoE: Hierarchical Task-Guided and Context-Responsive Routing for Neural Machine Translation—0
Two Experts Are All You Need for Steering Thinking: Reinforcing Cognitive Effort in MoE Reasoning Models Without Additional Training—0
FuxiMT: Sparsifying Large Language Models for Chinese-Centric Multilingual Machine Translation—0
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